{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Sebastian Raschka](http://www.sebastianraschka.com)\n",
    "\n",
    "[back](https://github.com/rasbt/matplotlib-gallery) to the `matplotlib-gallery` at [https://github.com/rasbt/matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last updated: 2016-03-12 \n",
      "\n",
      "CPython 3.5.1\n",
      "IPython 4.0.3\n",
      "\n",
      "matplotlib 1.5.1\n",
      "numpy 1.10.4\n"
     ]
    }
   ],
   "source": [
    "%watermark -u -v -d -p matplotlib,numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1.5em\">[More info](http://nbviewer.ipython.org/github/rasbt/watermark) about the `%watermark` extension</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar plots in Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Bar plot with error bars](#Bar-plot-with-error-bars)\n",
    "- [Horizontal bar plot with error bars](#Horizontal-bar-plot-with-error-bars)\n",
    "- [Back-to-back bar plot](#Back-to-back-bar-plot)\n",
    "- [Grouped bar plot](#Grouped-bar-plot)\n",
    "- [Stacked bar plot](#Stacked-bar-plot)\n",
    "- [Bar plot with plot labels/text 1](#Bar-plot-with-plot-labels/text-1)\n",
    "- [Bar plot with plot labels/text 2](#Bar-plot-with-plot-labels/text-2)\n",
    "- [Barplot with auto-rotated labels and text](#Barplot-with-auto-rotated-labels-and-text)\n",
    "- [Bar plot with color gradients](#Bar-plot-with-color-gradients)\n",
    "- [Bar plot pattern fill](#Bar-plot-pattern-fill)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar plot with error bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AscDlmFaSiIhkZKzC/x5Mwe9kvMI/HC9gjhM8jDkzKFn4T8Fc5RPMJZ8HgEHgyXG2XSjq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10544cd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# input data\n",
    "mean_values = [1, 2, 3]\n",
    "variance = [0.2, 0.4, 0.5]\n",
    "bar_labels = ['bar 1', 'bar 2', 'bar 3']\n",
    "\n",
    "# plot bars\n",
    "x_pos = list(range(len(bar_labels)))\n",
    "plt.bar(x_pos, mean_values, yerr=variance, align='center', alpha=0.5)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "# set height of the y-axis\n",
    "max_y = max(zip(mean_values, variance)) # returns a tuple, here: (3, 5)\n",
    "plt.ylim([0, (max_y[0] + max_y[1]) * 1.1])\n",
    "\n",
    "# set axes labels and title\n",
    "plt.ylabel('variable y')\n",
    "plt.xticks(x_pos, bar_labels)\n",
    "plt.title('Bar plot with error bars')\n",
    "\n",
    "plt.show()\n",
    "#plt.savefig('./my_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Horizontal bar plot with error bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M2/tk/JGkSi0+4DFgUd3D0Cy9/OV1j2A8+dnpkpSB3A+n587PTpckqTAW8Yrl3HME86Uu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\nkiRJkiRJkiRJkiSpWv8fXS6o794F/l0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1054e2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# input data\n",
    "mean_values = [1, 2, 3]\n",
    "std_dev = [0.2, 0.4, 0.5]\n",
    "bar_labels = ['bar 1', 'bar 2', 'bar 3']\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "# plot bars\n",
    "y_pos = np.arange(len(mean_values))\n",
    "y_pos = [x for x in y_pos]\n",
    "plt.yticks(y_pos, bar_labels, fontsize=10)\n",
    "plt.barh(y_pos, mean_values, xerr=std_dev, \n",
    "         align='center', alpha=0.4, color='g')\n",
    "\n",
    "# annotation and labels\n",
    "plt.xlabel('measurement x')\n",
    "t = plt.title('Bar plot with standard deviation')\n",
    "plt.ylim([-1,len(mean_values)+0.5])\n",
    "plt.xlim([0, 4])\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Back-to-back bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BXbM777yz6RKGyv5iy9xf5t7A/kaBA1uSpAAc2DXbtGlT0yUMlf3Flrm/zL2B\n/Y2Cxfi2rg3AQU0XIUlSjTYCq5ouQpIkSZIkSZIk9XUc8ADwW00XssA+QMkrNgDnA/s0W86C+zBw\nDaXHLwPLmi1nQb0KuAq4H3hmw7UspCOBHwHXAu9puJaFdgawBfhh04UMyT7ABZTfyyuBY5otZ8E9\nAriYsr28Gvj7ZssZit2Ay4GvN13IrtoH+DZwI/kG9qN7Lr8TOL2pQobkD5l+B8I/VF9ZPBXYn7KB\nzDKwdwOuAyaA3SkbxgOaLGiBvRB4BnkH9uOZPllpKTBFrucP4JHVv0uA7wOHNFjLMLwL+Czwtfnu\ntJjf1vVR4G+aLmJI7u65vBS4valChuRcypERKK+Mn9RgLQvtR8CPmy5igR1MGdibgN8AZwOvbLKg\nBXYhsLXpIoZoM+VFFsAvKEe39m6unKH4ZfXvwykvMH/eYC0L7UnASyg7biE//OOVwM3AFU0XMkQf\nBH4CvIFce6AzvRH4ZtNFaF5PBG7qWb65uk7xTFCOJlzccB0L7WGUFyVbKEe3rm62nAV1IvBupndy\n5rRk+LXM6VzKoZyZ3ge8F/ijnusW4/vF+5mrv/9BySneV339LeUJ+4v6SlsQ/fqD0t+vgX+vq6gF\nMkhvmfh5tjksBb4IHEvZ087kAcph/2XAd4BJoNVgPQvlZcCtlPx6stlSds0fUF5F3Vh9/YZyqG6v\nBmsapn0pJ4pksxr4HuWEkYwyZdjPpZwv0vVe8p14NkHeDBvKuQffAf6q6UJqcDzw100XsUA+RDm6\ndSPwM+Ae4N8areghynjS2VN6Lr8T+ExThQzJkZQzVh/bdCFDdAHwX5ouYoEsAa6nDLWHk++kM8g9\nsMcoG/kTmy5kSB4LLK8u7wmsA45orpyheREJjuDdQL6B/UXKxmMD8CXyHT24FmhTDvNcDnyq2XIW\n1J9SXhH/inKyz7eaLWfBvJhydvF1lD3sTD4H3ALcR3nuosVP/RxCOWS8gen/c0c2WtHCehpwGaW/\nKyh5b0Yvos9Z4pIkSZIkSZIkSZIkSZIkSZIkSZKkWu3WdAGSRtKLKB9QcXPThUhRLNYP/5BUnyZe\nuB8GPL+Bx5UkaSATlI/o/DTlL4t9lvJBN9+jfGzns6v7PQo4g/KpS5cBr+j5/nXAD6qv51XXP6G6\n/nLKX9F7QXV974dAHFU9LsBa4F8ony38z8B+lL/atr5az8qe+30KuIjy50sngTMpn5bUXRdVD/9Z\n1fSFqn6pXhemAAACb0lEQVQonwOwprr+imq9E5S/m3xzVe/Mzzb+GOXvRQP8MfBdJEmq2QTlA21+\nn/I3oNcD/1rd9grgK9XlDwGvqy4vpwz3R1L+lvIe1fVPAS6tLh9H+TQxKEfOllaXez97/c/ZcWB/\njelPwjsf+L3q8nOq5e79up+29grgrhm1H0T5W8/frWqD8sEh3YF7I/CO6vJ/B06rLp8AvIvZ7Un5\nQJzDKC9unjzH/aSR0uTHa0qj6kbKh6NQ/XtedflKykCHssf6cqY/lWgPYB/K3y//BGVQ3s/0B8lc\nQtkj3x34KrCxTw0d4Jzq36WUPfVzem5/eM/9uh9IcGX1+L21T1R1HUjZw+5+b/cywJerfy8D/qzn\n+rk+NvdXwJuBCykfFXljn16kkeDAlup3X8/lByifGd693Pt/8s8oH6TSaw3lcPJ/o2TP91bXXwi8\nkPL5umuBj1I+Ba73s673ZEe/rP59GHAn8Iw56u2tb2btSygvHM4FXjvH93e/534G3+Y8HbgNeOKA\n95fS86QzaXH6DnBMz3J3mD6GspcL8HqmTxjblzLgTqccYu/efwvwVMr/9T9lxwHedRdlL/aoanmM\nMjAH0aHk4C+g5OBQ8uunzPkdxd3Ao+e4bZxyuPwZlE8RO3jAWqTUHNhS/WYOzc4slz9AObx9BeVQ\n9N9V138KeAPlowZXMn1S2WHVdZcBrwJOqq7/W+AblJPabpnncV8HvKlax5VMn+Q2V329bgdWUz7G\nciPlcPjKWe7X6fn+r1NeQFzO9AlyUF4snE7J5DdXNZ3O9CF6SZIkSZIkSZIkSZIkSZIkSZIkSZIk\nSZIkSZIUxf8HolA96eMSG00AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105463c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# input data\n",
    "X1 = np.array([1, 2, 3])\n",
    "X2 = np.array([2, 2, 3])\n",
    "\n",
    "\n",
    "bar_labels = ['bar 1', 'bar 2', 'bar 3']\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "# plot bars\n",
    "y_pos = np.arange(len(X1))\n",
    "y_pos = [x for x in y_pos]\n",
    "plt.yticks(y_pos, bar_labels, fontsize=10)\n",
    "\n",
    "\n",
    "plt.barh(y_pos, X1, \n",
    "         align='center', alpha=0.4, color='g')\n",
    "\n",
    "# we simply negate the values of the numpy array for\n",
    "# the second bar:\n",
    "plt.barh(y_pos, -X2,\n",
    "         align='center', alpha=0.4, color='b')\n",
    "\n",
    "# annotation and labels\n",
    "plt.xlabel('measurement x')\n",
    "t = plt.title('Bar plot with standard deviation')\n",
    "plt.ylim([-1,len(X1)+0.1])\n",
    "plt.xlim([-max(X2)-1, max(X1)+1])\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouped bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Fucedgra2Ns444wymTp3Kd7/7XWbPns2sWbMAePvb386hhx7KTTfdxNq1a/nFL37BP//z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\nJElS/f4/igcH2I+N1LkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10686cbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Input data\n",
    "green_data = [1, 2, 3]\n",
    "blue_data = [3, 2, 1]\n",
    "red_data = [2, 3, 3]\n",
    "labels = ['group 1', 'group 2', 'group 3']\n",
    "\n",
    "# Setting the positions and width for the bars\n",
    "pos = list(range(len(green_data))) \n",
    "width = 0.2 \n",
    "    \n",
    "# Plotting the bars\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "plt.bar(pos, green_data, width,\n",
    "                 alpha=0.5,\n",
    "                 color='g',\n",
    "                 label=labels[0])\n",
    "\n",
    "plt.bar([p + width for p in pos], blue_data, width,\n",
    "                 alpha=0.5,\n",
    "                 color='b',\n",
    "                 label=labels[1])\n",
    "    \n",
    "plt.bar([p + width*2 for p in pos], red_data, width,\n",
    "                 alpha=0.5,\n",
    "                 color='r',\n",
    "                 label=labels[2])\n",
    "\n",
    "# Setting axis labels and ticks\n",
    "ax.set_ylabel('y-value')\n",
    "ax.set_title('Grouped bar plot')\n",
    "ax.set_xticks([p + 1.5 * width for p in pos])\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "# Setting the x-axis and y-axis limits\n",
    "plt.xlim(min(pos)-width, max(pos)+width*4)\n",
    "plt.ylim([0, max(green_data + blue_data + red_data) * 1.5])\n",
    "\n",
    "# Adding the legend and showing the plot\n",
    "plt.legend(['green', 'blue', 'red'], loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacked bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8DQujfhRK3QiD8rRXzJlma7/CPBM2ADfxvlPwuAd3NeSLwP3ABbgJ+JOC978W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\nIiIiIiIiIiIiIiIiIiJF4f8HNpdQ7Z0jvOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104e6fb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "blue_data = [100,120,140]\n",
    "red_data = [150,120,190]\n",
    "green_data = [80,70,90]\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
    "\n",
    "bar_width = 0.5\n",
    "\n",
    "# positions of the left bar-boundaries\n",
    "bar_l = [i+1 for i in range(len(blue_data))] \n",
    "\n",
    "# positions of the x-axis ticks (center of the bars as bar labels)\n",
    "tick_pos = [i+(bar_width/2) for i in bar_l] \n",
    "\n",
    "###################\n",
    "## Absolute count\n",
    "###################\n",
    "\n",
    "ax1.bar(bar_l, blue_data, width=bar_width,\n",
    "        label='blue data', alpha=0.5, color='b')\n",
    "ax1.bar(bar_l, red_data, width=bar_width,\n",
    "        bottom=blue_data, label='red data', alpha=0.5, color='r')\n",
    "ax1.bar(bar_l, green_data, width=bar_width,\n",
    "        bottom=[i+j for i,j in zip(blue_data,red_data)], label='green data', alpha=0.5, color='g')\n",
    "\n",
    "plt.sca(ax1)\n",
    "plt.xticks(tick_pos, ['category 1', 'category 2', 'category 3'])\n",
    "\n",
    "ax1.set_ylabel(\"Count\")\n",
    "ax1.set_xlabel(\"\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlim([min(tick_pos)-bar_width, max(tick_pos)+bar_width])\n",
    "plt.grid()\n",
    "\n",
    "# rotate axis labels\n",
    "plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n",
    "\n",
    "############\n",
    "## Percent\n",
    "############\n",
    "\n",
    "totals = [i+j+k for i,j,k in zip(blue_data, red_data, green_data)]\n",
    "blue_rel = [i / j * 100 for  i,j in zip(blue_data, totals)]\n",
    "red_rel = [i / j * 100 for  i,j in zip(red_data, totals)]\n",
    "green_rel = [i / j * 100 for  i,j in zip(green_data, totals)]\n",
    "\n",
    "ax2.bar(bar_l, blue_rel, \n",
    "        label='blue data', alpha=0.5, color='b', width=bar_width\n",
    "        )\n",
    "ax2.bar(bar_l, red_rel, \n",
    "        bottom=blue_rel, label='red data', alpha=0.5, color='r', width=bar_width\n",
    "        )\n",
    "ax2.bar(bar_l, green_rel, \n",
    "        bottom=[i+j for i,j in zip(blue_rel, red_rel)], \n",
    "        label='green data', alpha=0.5, color='g', width=bar_width\n",
    "        )\n",
    "\n",
    "plt.sca(ax2)\n",
    "plt.xticks(tick_pos, ['category 1', 'category 2', 'category 3'])\n",
    "ax2.set_ylabel(\"Percentage\")\n",
    "ax2.set_xlabel(\"\")\n",
    "\n",
    "plt.xlim([min(tick_pos)-bar_width, max(tick_pos)+bar_width])\n",
    "plt.grid()\n",
    "\n",
    "# rotate axis labels\n",
    "plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar plot with plot labels/text 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MyJQT1rHEzfjFy9hJcTIhkyQpTziXZeGyhkySJKmfrCGTJEmKnAmZcsI6lrgZv3gZu7gZ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\nf8GrwVF/DlRfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106791750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data = range(200, 225, 5)\n",
    "\n",
    "bar_labels = ['a', 'b', 'c', 'd', 'e']\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "# plot bars\n",
    "y_pos = np.arange(len(data))\n",
    "plt.yticks(y_pos, bar_labels, fontsize=16)\n",
    "bars = plt.barh(y_pos, data,\n",
    "         align='center', alpha=0.4, color='g')\n",
    "\n",
    "# annotation and labels\n",
    "\n",
    "for b,d in zip(bars, data):\n",
    "    plt.text(b.get_width() + b.get_width()*0.08, b.get_y() + b.get_height()/2,\n",
    "        '{0:.2%}'.format(d/min(data)), \n",
    "        ha='center', va='bottom', fontsize=12)\n",
    "\n",
    "plt.xlabel('X axis label', fontsize=14)\n",
    "plt.ylabel('Y axis label', fontsize=14)\n",
    "t = plt.title('Bar plot with plot labels/text', fontsize=18)\n",
    "plt.ylim([-1,len(data)+0.5])\n",
    "plt.vlines(min(data), -1, len(data)+0.5, linestyles='dashed')\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar plot with plot labels/text 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MfiIilWHwExGpDIOfiEhlGPxERCrD4CciUhkGPxGRyjD4iYhUhsFPRKQyigW/1WrF6NGj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\npDIMfiIilfl/4XVx3zrYTS4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106886cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# input data\n",
    "mean_values = [1, 2, 3]\n",
    "bar_labels = ['bar 1', 'bar 2', 'bar 3']\n",
    "\n",
    "# plot bars\n",
    "x_pos = list(range(len(bar_labels)))\n",
    "rects = plt.bar(x_pos, mean_values, align='center', alpha=0.5)\n",
    "\n",
    "# label bars\n",
    "def autolabel(rects):\n",
    "    for ii,rect in enumerate(rects):\n",
    "        height = rect.get_height()\n",
    "        plt.text(rect.get_x()+rect.get_width()/2., 1.02*height, '%s'% (mean_values[ii]),\n",
    "            ha='center', va='bottom')\n",
    "autolabel(rects)\n",
    "    \n",
    "\n",
    "\n",
    "# set height of the y-axis\n",
    "max_y = max(zip(mean_values, variance)) # returns a tuple, here: (3, 5)\n",
    "plt.ylim([0, (max_y[0] + max_y[1]) * 1.1])\n",
    "\n",
    "# set axes labels and title\n",
    "plt.ylabel('variable y')\n",
    "plt.xticks(x_pos, bar_labels)\n",
    "plt.title('Bar plot with labels')\n",
    "\n",
    "plt.show()\n",
    "#plt.savefig('./my_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Barplot with auto-rotated labels and text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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34pP0LuALwLnAb8h+Cvz/AH8CtAHfiuyXV9vXnwD8ObCJ7OaMXwCuC98Fu8dc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c84c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "idx = range(4)\n",
    "values = [100, 1000, 5000, 20000]\n",
    "labels = ['category 1', 'category 2',\n",
    "          'category 3', 'category 4']\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "\n",
    "# Automatically align and rotate tick labels:\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "bars = plt.bar(idx, values, align='center')\n",
    "plt.xticks(idx, labels)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Add text labels to the top of the bars\n",
    "def autolabel(bars):\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        ax.text(bar.get_x() + bar.get_width()/2., 1.05 * height,\n",
    "                '%d' % int(height),\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "autolabel(bars)\n",
    "plt.ylim([0, 25000])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar plot with color gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD7CAYAAABKfn7LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1052d6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as col\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# input data\n",
    "mean_values = range(10,18)\n",
    "x_pos = range(len(mean_values))\n",
    "\n",
    "\n",
    "# create colormap\n",
    "cmap1 = cm.ScalarMappable(col.Normalize(min(mean_values), max(mean_values), cm.hot))\n",
    "cmap2 = cm.ScalarMappable(col.Normalize(0, 20, cm.hot))\n",
    "\n",
    "# plot bars\n",
    "plt.subplot(121)\n",
    "plt.bar(x_pos, mean_values, align='center', alpha=0.5, color=cmap1.to_rgba(mean_values))\n",
    "plt.ylim(0, max(mean_values) * 1.1)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(x_pos, mean_values, align='center', alpha=0.5, color=cmap2.to_rgba(mean_values))\n",
    "plt.ylim(0, max(mean_values) * 1.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar plot pattern fill"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/cE2XEEIKhKZLCCEFQtMlhXDo0CG8++67ZZeRSBRIRJL5xz/+gbGxMTzyyCN4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\ngfwfMw2kQLlaaKcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1067b5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "patterns = ('-', '+', 'x', '\\\\', '*', 'o', 'O', '.')\n",
    "\n",
    "fig = plt.gca()\n",
    "\n",
    "# input data\n",
    "mean_values = range(1, len(patterns)+1)\n",
    "\n",
    "# plot bars\n",
    "x_pos = list(range(len(mean_values)))\n",
    "bars = plt.bar(x_pos, \n",
    "               mean_values, \n",
    "               align='center', \n",
    "               color='white',\n",
    "               )\n",
    "\n",
    "# set patterns\n",
    "for bar, pattern in zip(bars, patterns):\n",
    "     bar.set_hatch(pattern)\n",
    "        \n",
    "\n",
    "# set axes labels and formatting\n",
    "fig.axes.get_yaxis().set_visible(False)        \n",
    "plt.ylim([0, max(mean_values) * 1.1])\n",
    "plt.xticks(x_pos, patterns)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
